• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

Vol. 14, No. 9, September 30, 2020
10.3837/tiis.2020.09.011, Download Paper (Free):

Abstract

Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets. Keywords


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Cite this article

[IEEE Style]
X. Wang, X. Zhang and C. Ning, "Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning," KSII Transactions on Internet and Information Systems, vol. 14, no. 9, pp. 3762-3781, 2020. DOI: 10.3837/tiis.2020.09.011.

[ACM Style]
Xin Wang, Xin Zhang, and Chen Ning. 2020. Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning. KSII Transactions on Internet and Information Systems, 14, 9, (2020), 3762-3781. DOI: 10.3837/tiis.2020.09.011.